Title :
Detecting Myocardial Infraction Using VCG Leads
Author_Institution :
Sch. of Inf. & Electron. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou
Abstract :
Standard electrocardiogram (ECG) lead system and Frank vectorcardiogram (VCG) lead system are two most popular and basic lead systems. Most of the existing analyses on ECG signals are based on the standard ECG leads. In practice, Frank VCG leads that are orthogonal between them are more correlated with anatomy than standard ECG leads. The VCG feature extraction from Frank leads has been studied in this research. Myocardial infraction (MI) VCG signals including health control (HC), acute MI (AMI), sub-acute MI (SAMI) that were taken from PTB diagnostic ECG database were employed for the analysis in this study. The multivariate autoregressive (AR) coefficients were utilized as VCG features for the classification. The results show that Frank VCG leads can classify better compared to standard ECG leads.
Keywords :
autoregressive processes; diseases; electrocardiography; feature extraction; medical signal processing; signal classification; vectors; Frank vectorcardiogram; PTB diagnostic ECG database; VCG lead system; acute MI; electrocardiogram; feature extraction; health control; multivariate autoregressive coefficient; myocardial infraction detection; sub-acute MI; Ambient intelligence; Brain modeling; Cardiology; Electrocardiography; Electrodes; Feature extraction; Heart rate; Myocardium; Sampling methods; Spatial databases;
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1747-6
Electronic_ISBN :
978-1-4244-1748-3
DOI :
10.1109/ICBBE.2008.885